Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function.

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dc.contributor.authorSouza, Paulo Vitor de Campos
dc.contributor.authorTorres, Luiz Carlos Bambirra
dc.contributor.authorGuimarães, Augusto Júnio
dc.contributor.authorAraújo, Vanessa Souza
dc.contributor.authorAraújo, Vinicius Jonathan Silva
dc.contributor.authorRezende, Thiago Silva
dc.date.accessioned2022-09-15T20:40:05Z
dc.date.available2022-09-15T20:40:05Z
dc.date.issued2019pt_BR
dc.description.abstractThis paper proposes the use of fuzzification functions based on clustering of data based on their density to perform the granularization of the input space. The neurons formed in this layer are built through the density centers obtained with the input data of the model. In the second layer, the nullneurons aggregate the generated neurons in the first layer and allow the creation of if/then fuzzy rules. Even in the second layer, a regularization function is activated to determine the essential nullneurons. The concepts of extreme learning machine generate the weights used in the third layer, but with a regularizing factor. Finally, in the third layer, represented by an artificial neural network, it has a single neuron that the activation function uses robust functions to carry out the model. To verify the new training approach for fuzzy neural networks, we performed real and synthetic database tests for the pattern classification, which led to the conclusion that the data density-based approach the use of regularization factors in the second model layer and neurons with more robust activation functions allowed better results compared to other classifiers that use the concepts of extreme learning machine.pt_BR
dc.identifier.citationSOUZA, P. V. de C. et al. Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function. Soft Computing, v. 23, p. 12475-12489, 2019. Disponível em: <https://link.springer.com/article/10.1007/s00500-019-03792-z>. Acesso em: 29 abr. 2022.pt_BR
dc.identifier.doihttps://doi.org/10.1007/s00500-019-03792-zpt_BR
dc.identifier.issn1433-7479
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/15310
dc.identifier.uri2https://link.springer.com/article/10.1007/s00500-019-03792-zpt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectPattern classificationpt_BR
dc.titleData density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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